We present an enhanced YOLOv8 real time vehicle detection and classification framework, for estimating carbon emissions in urban environments. The system enhances YOLOv8 architecture to detect, segment, and track vehicles from live traffic video streams. Once a vehicle is localized, a dedicated deep learning-based identification module is employed to recognize license plates and classify vehicle types. Since YOLOv8 lacks the built-in capacity for fine grained recognition tasks such as reading license plates or determining vehicle attributes beyond class labels, our framework incorporates a hybrid pipeline where each detected vehicle is tracked and its bounding box is cropped and passed to a deep Optical Character Recognition (OCR) module. This OCR system, composed of multiple convolutional neural network (CNN) layers, is trained specifically for character-level detection and license plate decoding under varied conditions such as motion blur, occlusion, and diverse font styles. Additionally, the recognized plate information is validated using a real time API that cross references with an external vehicle registration database to ensure accurate classification and emission estimation. This multi-stage approach enables precise, automated calculation of per vehicle carbon emissions. Extensive evaluation was conducted using a diverse vehicle dataset enriched with segmentation masks and annotated license plates. The YOLOv8 detector achieved a mean Average Precision (mAP@0.5) of approximately 71% for bounding boxes and 70% for segmentation masks. Character level OCR accuracy reached up to 99% with the best performing CNN model. These results affirm the feasibility of combining real time object detection with deep OCR for practical deployment in smart transportation systems, offering a scalable solution for automated, vehicle specific carbon emission monitoring.

The rapid integration of Artificial Intelligence (AI) in Higher Education (HE) is transforming personalized learning, administrative automation, and decision-making. However, this progress presents a duality, as AI adoption also introduces ethical and institutional challenges, including algorithmic bias, data privacy risks, and governance inconsistencies. To address these concerns, this study introduces the Human-Driven AI in Higher Education (HD-AIHED) Framework, ensuring compliance with UNESCO and OECD ethical standards. This conceptual research employs a qualitative meta-synthesis approach, integrating qualitative and quantitative studies to identify patterns, contradictions, and gaps in AI adoption within HE. It reinterprets existing datasets through theoretical and ethical lenses to develop governance frameworks. The study applies a participatory integrated co-system, Phased Human Intelligence, SWOC analysis, and AI ethical review boards to assess AI readiness and governance strategies for universities and HE institutions. The HD-AIHED model bridges AI research gaps, addresses global real-time challenges, and provides tailored, scalable, and ethical strategies for diverse educational contexts. By emphasizing interdisciplinary collaboration among stakeholders, this study envisions AIHED as a transparent and equitable force for innovation. The HD-AIHED framework ensures AI acts as a collaborative and ethical enabler rather than a disruptive replacement for human intelligence while advocating for responsible AI implementation in HE.

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upper and lower limb execution of motor and motor imagery tasks has been unveiled. Despite this, the productivity of the Machine Learning (ML) Models that were trained on this dataset was alarmingly deficient, and the evaluation framework seemed insufficient. To enhance outcomes, robust feature engineering (signal processing) methodologies are implemented. A collection of time domain, frequency domain, and wavelet-derived features was obtained from 16-channel EEG signals, and the Maximum Relevance Minimum Redundancy (MRMR) approach was employed to identify the four most significant features. For classification K Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Na\"ive Bayes (NB) models were implemented with these selected features, evaluating their effectiveness through metrics such as testing accuracy, precision, recall, and F1 Score. By leveraging SVM with a Gaussian Kernel, a remarkable maximum testing accuracy of 92.50% for motor activities and 95.48% for imagery activities is achieved. These results are notably more dependable and gratifying compared to the previous study, where the peak accuracy was recorded at 74.36%. This research work provides an in-depth analysis of the MI Limb EEG dataset and it will help in designing and developing simple, cost-effective and reliable BCI systems for neuro-rehabilitation.

Knowledge graph completion (KGC) is a task of inferring missing triples based on existing Knowledge Graphs (KGs). Both structural and semantic information are vital for successful KGC. However, existing methods only use either the structural knowledge from the KG embeddings or the semantic information from pre-trained language models (PLMs), leading to suboptimal model performance. Moreover, since PLMs are not trained on KGs, directly using PLMs to encode triples may be inappropriate. To overcome these limitations, we propose a novel framework called Bridge, which jointly encodes structural and semantic information of KGs. Specifically, we strategically encode entities and relations separately by PLMs to better utilize the semantic knowledge of PLMs and enable structured representation learning via a structural learning principle. Furthermore, to bridge the gap between KGs and PLMs, we employ a self-supervised representation learning method called BYOL to fine-tune PLMs with two different views of a triple. Unlike BYOL, which uses augmentation methods to create two semantically similar views of the same image, potentially altering the semantic information. We strategically separate the triple into two parts to create different views, thus avoiding semantic alteration. Experiments demonstrate that Bridge outperforms the SOTA models on three benchmark datasets.

This study addresses the classification of defects in apples as a crucial measure to mitigate economic losses and optimize the food supply chain. An innovative approach is employed that integrates images from the visible spectrum and 660 nm spectral wavelength to enhance accuracy and efficiency in defect classification. The methodology is based on the use of Single-Input and Multi-Inputs convolutional neural networks (CNNs) to validate the proposed strategies. Steps include image acquisition and preprocessing, classification model training, and performance evaluation. Results demonstrate that defect classification using the 660 nm spectral wavelength reveals details not visible in the entire visible spectrum. It is seen that the use of the appropriate spectral range in the classification process is slightly superior to the entire visible spectrum. The MobileNetV1 model achieves an accuracy of 98.80\% on the validation dataset versus the 98.26\% achieved using the entire visible spectrum. Conclusions highlight the potential to enhance the method by capturing images with specific spectral ranges using filters, enabling more effective network training for classification task. These improvements could further enhance the system's capability to identify and classify defects in apples.

This study presents an investigation into the utilization of a Multi-Input architecture for the classification of fruits (apples and mangoes) into healthy and defective states, employing both RGB and silhouette images. The primary aim is to enhance the accuracy of CNN models. The methodology encompasses image acquisition, preprocessing of datasets, training, and evaluation of two CNN models: MobileNetV2 and VGG16. Results reveal that the inclusion of silhouette images alongside the Multi-Input architecture yields models with superior performance compared to using only RGB images for fruit classification, whether healthy or defective. Specifically, optimal results were achieved using the MobileNetV2 model, achieving 100\% accuracy. This finding suggests the efficacy of this combined methodology in improving the precise classification of healthy or defective fruits, which could have significant implications for applications related to external quality inspection of fruits.

This document presents the study of the problem of location and trajectory that a robot must follow. It focuses on applying the Kalman filter to achieve location and trajectory estimation in an autonomous mobile differential robot. The experimental data was carried out through tests obtained with the help of two incremental encoders that are part of the construction of the differential robot. The data transmission is carried out from a PC where the control is carried out with the Matlab/Simulink software. The results are expressed in graphs showing the path followed by the robot using PI control, the estimator of the Kalman filter in a real system.

Another substantial as key historical resources, contain a diverse project is the "Digging into Data Challenge". A range of information about political, economic, part of the Transatlantic Partnership for Social Sciences and cultural processes and are abundant due to and Humanities 2016, this initiative yielded focused efforts to preserve them within national a vast collection of 19th-century press materials archives. Indeed, the discipline of Digital Humanities, known as "Atlas - Oceanic Exchanges. Tracing which emphasizes the incorporation of digital Global Information Networks in Historical Papers" tools in humanities and social sciences research, (Exchanges). Other significant works include "Viral has spent much of the past three decades on the Texts: Mapping Networks of Reprinting in 19th-task of digitization, resulting in a wealth of curated Century Newspapers and Magazines" (Cordell and digital collections (Berry and Fagerjord, 2017; Dobson, Smith), a project that investigates 19th-century journalistic 2019). However, digitizing these corpora has reports to understand the culture of reprinting brought plenty of challenges in transcribing the in the United States before the Civil War, and images into machine-readable texts.

For modeling the number of visits in Stopi\'{c}a cave (Serbia) we consider the classical Auto-regressive Integrated Moving Average (ARIMA) model, Machine Learning (ML) method Support Vector Regression (SVR), and hybrid NeuralPropeth method which combines classical and ML concepts. The most accurate predictions were obtained with NeuralPropeth which includes the seasonal component and growing trend of time-series. In addition, non-linearity is modeled by shallow Neural Network (NN), and Google Trend is incorporated as an exogenous variable. Modeling tourist demand represents great importance for management structures and decision-makers due to its applicability in establishing sustainable tourism utilization strategies in environmentally vulnerable destinations such as caves. The data provided insights into the tourist demand in Stopi\'{c}a cave and preliminary data for addressing the issues of carrying capacity within the most visited cave in Serbia.

Lexical Simplification (LS) automatically replaces difficult to read words for easier alternatives while preserving a sentence's original meaning. LS is a precursor to Text Simplification with the aim of improving text accessibility to various target demographics, including children, second language learners, individuals with reading disabilities or low literacy. Several datasets exist for LS. These LS datasets specialize on one or two sub-tasks within the LS pipeline. However, as of this moment, no single LS dataset has been developed that covers all LS sub-tasks. We present MultiLS, the first LS framework that allows for the creation of a multi-task LS dataset. We also present MultiLS-PT, the first dataset to be created using the MultiLS framework. We demonstrate the potential of MultiLS-PT by carrying out all LS sub-tasks of (1). lexical complexity prediction (LCP), (2). substitute generation, and (3). substitute ranking for Portuguese. Model performances are reported, ranging from transformer-based models to more recent large language models (LLMs).